{"action":"closed","issue":{"url":"https://api.github.com/repos/o19s/elasticsearch-learning-to-rank/issues/14","repository_url":"https://api.github.com/repos/o19s/elasticsearch-learning-to-rank","labels_url":"https://api.github.com/repos/o19s/elasticsearch-learning-to-rank/issues/14/labels{/name}","comments_url":"https://api.github.com/repos/o19s/elasticsearch-learning-to-rank/issues/14/comments","events_url":"https://api.github.com/repos/o19s/elasticsearch-learning-to-rank/issues/14/events","html_url":"https://github.com/o19s/elasticsearch-learning-to-rank/issues/14","id":212614539,"number":14,"title":"Support Fewer Models (slim down to handful of Ranklib models)","user":{"login":"softwaredoug","id":629060,"avatar_url":"https://avatars0.githubusercontent.com/u/629060?v=4","gravatar_id":"","url":"https://api.github.com/users/softwaredoug","html_url":"https://github.com/softwaredoug","followers_url":"https://api.github.com/users/softwaredoug/followers","following_url":"https://api.github.com/users/softwaredoug/following{/other_user}","gists_url":"https://api.github.com/users/softwaredoug/gists{/gist_id}","starred_url":"https://api.github.com/users/softwaredoug/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/softwaredoug/subscriptions","organizations_url":"https://api.github.com/users/softwaredoug/orgs","repos_url":"https://api.github.com/users/softwaredoug/repos","events_url":"https://api.github.com/users/softwaredoug/events{/privacy}","received_events_url":"https://api.github.com/users/softwaredoug/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2017-03-08T01:50:11Z","updated_at":"2017-09-15T18:18:37Z","closed_at":"2017-09-15T18:18:37Z","author_association":"OWNER","body":"Erik Bernhardson makes a good point that really there's only a few types of models you probably care about. Ranklib comes with all sorts of intermediate/weird models that most people probably don't care about.\r\n\r\n## Maximum Interpretability\r\n\r\n- Models, like the **linear model**, that are basically optimized boosts. Advantage: it's easy to interpret, debug, and understand these models\r\n\r\n## Maximum Flexibility / Prediction Power\r\n\r\n- LambdaMART/Boosted Tree model that does a great job of predicting the weird nooks and crannies of search relevance\r\n\r\nRelated, we probably don't need to really rely on Ranklib when we just care about these 2 or 3 models, and only the evaluation parts of those models as well"}}
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